Overview

Dataset statistics

Number of variables16
Number of observations641
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.3%
Total size in memory80.3 KiB
Average record size in memory128.2 B

Variable types

Numeric14
Categorical2

Alerts

Dataset has 2 (0.3%) duplicate rowsDuplicates
p is highly overall correlated with cc and 3 other fieldsHigh correlation
t is highly overall correlated with sHigh correlation
ts is highly overall correlated with tdHigh correlation
td is highly overall correlated with tsHigh correlation
c is highly overall correlated with cnhdlHigh correlation
hdl is highly overall correlated with chdlHigh correlation
cc is highly overall correlated with p and 3 other fieldsHigh correlation
pp is highly overall correlated with p and 3 other fieldsHigh correlation
imc is highly overall correlated with p and 3 other fieldsHigh correlation
cct is highly overall correlated with p and 3 other fieldsHigh correlation
cnhdl is highly overall correlated with c and 1 other fieldsHigh correlation
chdl is highly overall correlated with hdl and 1 other fieldsHigh correlation
s is highly overall correlated with tHigh correlation

Reproduction

Analysis started2023-12-29 11:52:33.728583
Analysis finished2023-12-29 11:52:44.073392
Duration10.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

e
Real number (ℝ)

Distinct52
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.984399
Minimum18
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.115363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q131
median41
Q350
95-th percentile60
Maximum71
Range53
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.926588
Coefficient of variation (CV)0.29100312
Kurtosis-0.91294798
Mean40.984399
Median Absolute Deviation (MAD)10
Skewness0.042576109
Sum26271
Variance142.24351
MonotonicityNot monotonic
2023-12-29T05:52:44.187512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 25
 
3.9%
52 24
 
3.7%
47 24
 
3.7%
43 24
 
3.7%
38 23
 
3.6%
24 22
 
3.4%
51 21
 
3.3%
33 20
 
3.1%
41 19
 
3.0%
46 19
 
3.0%
Other values (42) 420
65.5%
ValueCountFrequency (%)
18 2
 
0.3%
19 3
 
0.5%
20 5
 
0.8%
21 8
 
1.2%
22 17
2.7%
23 10
1.6%
24 22
3.4%
25 16
2.5%
26 19
3.0%
27 15
2.3%
ValueCountFrequency (%)
71 1
 
0.2%
70 3
 
0.5%
69 1
 
0.2%
66 1
 
0.2%
65 3
 
0.5%
64 3
 
0.5%
63 2
 
0.3%
62 2
 
0.3%
61 9
1.4%
60 10
1.6%

s
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1
442 
2
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters641
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Length

2023-12-29T05:52:44.240005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-29T05:52:44.284381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring characters

ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 641
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

u
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1
405 
4
119 
3
59 
2
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters641
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

Length

2023-12-29T05:52:44.320549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-29T05:52:44.365566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

Most occurring characters

ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 641
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 405
63.2%
4 119
 
18.6%
3 59
 
9.2%
2 58
 
9.0%

p
Real number (ℝ)

Distinct310
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.332112
Minimum40.5
Maximum124.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.414877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40.5
5-th percentile52
Q161.4
median69
Q378
95-th percentile96.25
Maximum124.5
Range84
Interquartile range (IQR)16.6

Descriptive statistics

Standard deviation13.180143
Coefficient of variation (CV)0.18739865
Kurtosis0.57590567
Mean70.332112
Median Absolute Deviation (MAD)8.45
Skewness0.69917326
Sum45082.884
Variance173.71617
MonotonicityNot monotonic
2023-12-29T05:52:44.466194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 14
 
2.2%
63 14
 
2.2%
73 13
 
2.0%
74 12
 
1.9%
69 11
 
1.7%
68 11
 
1.7%
64 11
 
1.7%
58 10
 
1.6%
71 10
 
1.6%
60 10
 
1.6%
Other values (300) 525
81.9%
ValueCountFrequency (%)
40.5 1
0.2%
41 1
0.2%
42 2
0.3%
43 1
0.2%
45.2 1
0.2%
46.7 1
0.2%
47.7 1
0.2%
47.95 1
0.2%
48 2
0.3%
48.1 1
0.2%
ValueCountFrequency (%)
124.5 1
0.2%
112.3 1
0.2%
110 1
0.2%
109.9 1
0.2%
109 1
0.2%
108 1
0.2%
107.8 1
0.2%
106.75 2
0.3%
105 1
0.2%
104 1
0.2%

t
Real number (ℝ)

Distinct45
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6015757
Minimum1.38
Maximum1.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.516906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile1.48
Q11.54
median1.59
Q31.66
95-th percentile1.75
Maximum1.91
Range0.53
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.085736775
Coefficient of variation (CV)0.053532766
Kurtosis0.12127825
Mean1.6015757
Median Absolute Deviation (MAD)0.06
Skewness0.52046523
Sum1026.61
Variance0.0073507947
MonotonicityNot monotonic
2023-12-29T05:52:44.571724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.54 33
 
5.1%
1.6 33
 
5.1%
1.55 32
 
5.0%
1.56 32
 
5.0%
1.57 31
 
4.8%
1.52 30
 
4.7%
1.59 29
 
4.5%
1.58 25
 
3.9%
1.5 25
 
3.9%
1.62 25
 
3.9%
Other values (35) 346
54.0%
ValueCountFrequency (%)
1.38 1
 
0.2%
1.42 1
 
0.2%
1.43 2
 
0.3%
1.44 5
 
0.8%
1.45 7
 
1.1%
1.47 12
1.9%
1.48 11
1.7%
1.49 11
1.7%
1.5 25
3.9%
1.51 20
3.1%
ValueCountFrequency (%)
1.91 1
 
0.2%
1.89 2
 
0.3%
1.85 1
 
0.2%
1.83 3
0.5%
1.82 5
0.8%
1.81 1
 
0.2%
1.8 1
 
0.2%
1.79 4
0.6%
1.78 5
0.8%
1.77 3
0.5%

ts
Real number (ℝ)

Distinct84
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.68848
Minimum80
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.627129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile96
Q1107
median118
Q3128
95-th percentile148
Maximum185
Range105
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.929419
Coefficient of variation (CV)0.134212
Kurtosis0.49911017
Mean118.68848
Median Absolute Deviation (MAD)10
Skewness0.60269512
Sum76079.316
Variance253.74638
MonotonicityNot monotonic
2023-12-29T05:52:44.681632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 35
 
5.5%
100 27
 
4.2%
110 25
 
3.9%
121 22
 
3.4%
117 20
 
3.1%
123 19
 
3.0%
124 17
 
2.7%
105 17
 
2.7%
108 16
 
2.5%
111 16
 
2.5%
Other values (74) 427
66.6%
ValueCountFrequency (%)
80 1
 
0.2%
84 1
 
0.2%
85 1
 
0.2%
86 1
 
0.2%
87 3
0.5%
88 1
 
0.2%
89 1
 
0.2%
90 6
0.9%
91 1
 
0.2%
92 3
0.5%
ValueCountFrequency (%)
185 1
 
0.2%
170 1
 
0.2%
165 2
0.3%
164 1
 
0.2%
163 2
0.3%
162 2
0.3%
160 4
0.6%
159 1
 
0.2%
158 1
 
0.2%
155 1
 
0.2%

td
Real number (ℝ)

Distinct53
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.34033
Minimum50
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.734547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median75
Q381
95-th percentile89
Maximum115
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8575959
Coefficient of variation (CV)0.11756779
Kurtosis0.37068417
Mean75.34033
Median Absolute Deviation (MAD)6
Skewness0.041035907
Sum48293.151
Variance78.457004
MonotonicityNot monotonic
2023-12-29T05:52:44.784808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 50
 
7.8%
80 37
 
5.8%
75 29
 
4.5%
77 28
 
4.4%
81 28
 
4.4%
73 27
 
4.2%
72 26
 
4.1%
60 24
 
3.7%
74 23
 
3.6%
76 22
 
3.4%
Other values (43) 347
54.1%
ValueCountFrequency (%)
50 2
 
0.3%
51 2
 
0.3%
52 1
 
0.2%
53 1
 
0.2%
54 1
 
0.2%
56 2
 
0.3%
57 2
 
0.3%
58 2
 
0.3%
59 3
 
0.5%
60 24
3.7%
ValueCountFrequency (%)
115 1
 
0.2%
103 1
 
0.2%
99 2
 
0.3%
98 1
 
0.2%
96 3
 
0.5%
94 3
 
0.5%
93 1
 
0.2%
92 2
 
0.3%
91 4
 
0.6%
90 13
2.0%

gs
Real number (ℝ)

Distinct81
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.377535
Minimum64
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.838484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile76
Q184
median90
Q398
95-th percentile135
Maximum378
Range314
Interquartile range (IQR)14

Descriptive statistics

Standard deviation29.92085
Coefficient of variation (CV)0.31045461
Kurtosis30.051489
Mean96.377535
Median Absolute Deviation (MAD)7
Skewness4.8604598
Sum61778
Variance895.25724
MonotonicityNot monotonic
2023-12-29T05:52:44.890475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 33
 
5.1%
93 27
 
4.2%
86 27
 
4.2%
88 27
 
4.2%
87 26
 
4.1%
95 25
 
3.9%
89 23
 
3.6%
83 22
 
3.4%
90 21
 
3.3%
80 21
 
3.3%
Other values (71) 389
60.7%
ValueCountFrequency (%)
64 1
 
0.2%
66 2
 
0.3%
71 4
 
0.6%
72 6
0.9%
73 8
1.2%
74 4
 
0.6%
75 6
0.9%
76 14
2.2%
77 10
1.6%
78 9
1.4%
ValueCountFrequency (%)
378 1
0.2%
317 1
0.2%
303 2
0.3%
262 1
0.2%
256 1
0.2%
255 2
0.3%
243 1
0.2%
236 1
0.2%
216 1
0.2%
208 1
0.2%

c
Real number (ℝ)

Distinct150
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.69111
Minimum85
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:44.946178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile133
Q1159
median182
Q3211
95-th percentile247
Maximum356
Range271
Interquartile range (IQR)52

Descriptive statistics

Standard deviation38.343897
Coefficient of variation (CV)0.20538684
Kurtosis1.061402
Mean186.69111
Median Absolute Deviation (MAD)25
Skewness0.58430747
Sum119669
Variance1470.2544
MonotonicityNot monotonic
2023-12-29T05:52:45.002229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 13
 
2.0%
204 12
 
1.9%
173 11
 
1.7%
176 11
 
1.7%
167 11
 
1.7%
161 11
 
1.7%
172 10
 
1.6%
158 10
 
1.6%
187 10
 
1.6%
242 10
 
1.6%
Other values (140) 532
83.0%
ValueCountFrequency (%)
85 1
 
0.2%
94 2
0.3%
97 1
 
0.2%
99 3
0.5%
108 1
 
0.2%
109 1
 
0.2%
110 1
 
0.2%
115 1
 
0.2%
117 2
0.3%
119 2
0.3%
ValueCountFrequency (%)
356 1
0.2%
347 1
0.2%
341 1
0.2%
316 1
0.2%
302 1
0.2%
301 1
0.2%
295 1
0.2%
290 1
0.2%
282 1
0.2%
281 1
0.2%

hdl
Real number (ℝ)

Distinct57
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.872075
Minimum21
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.056434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile29
Q137
median43
Q350
95-th percentile63
Maximum96
Range75
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.635841
Coefficient of variation (CV)0.24242849
Kurtosis2.243345
Mean43.872075
Median Absolute Deviation (MAD)7
Skewness1.0082697
Sum28122
Variance113.12111
MonotonicityNot monotonic
2023-12-29T05:52:45.109962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 35
 
5.5%
42 32
 
5.0%
40 31
 
4.8%
39 29
 
4.5%
46 28
 
4.4%
45 26
 
4.1%
41 23
 
3.6%
33 22
 
3.4%
34 21
 
3.3%
50 21
 
3.3%
Other values (47) 373
58.2%
ValueCountFrequency (%)
21 1
 
0.2%
24 3
 
0.5%
25 3
 
0.5%
26 1
 
0.2%
27 10
1.6%
28 12
1.9%
29 6
 
0.9%
30 11
1.7%
31 19
3.0%
32 15
2.3%
ValueCountFrequency (%)
96 1
0.2%
93 2
0.3%
84 1
0.2%
79 1
0.2%
78 1
0.2%
77 1
0.2%
76 1
0.2%
74 1
0.2%
72 2
0.3%
70 2
0.3%

cc
Real number (ℝ)

Distinct217
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89566453
Minimum0.52
Maximum1.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.167536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile0.71
Q10.82
median0.89
Q30.974
95-th percentile1.09
Maximum1.23
Range0.71
Interquartile range (IQR)0.154

Descriptive statistics

Standard deviation0.11551833
Coefficient of variation (CV)0.128975
Kurtosis-0.033444435
Mean0.89566453
Median Absolute Deviation (MAD)0.077
Skewness0.03748469
Sum574.12096
Variance0.013344484
MonotonicityNot monotonic
2023-12-29T05:52:45.221806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95 24
 
3.7%
0.93 19
 
3.0%
1.02 19
 
3.0%
0.9 19
 
3.0%
0.84 18
 
2.8%
0.85 18
 
2.8%
0.89 17
 
2.7%
0.82 16
 
2.5%
0.96 14
 
2.2%
0.76 14
 
2.2%
Other values (207) 463
72.2%
ValueCountFrequency (%)
0.52 1
 
0.2%
0.53 1
 
0.2%
0.55 1
 
0.2%
0.57 1
 
0.2%
0.6 1
 
0.2%
0.65 1
 
0.2%
0.66 4
0.6%
0.67 3
 
0.5%
0.68 2
 
0.3%
0.69 9
1.4%
ValueCountFrequency (%)
1.23 1
 
0.2%
1.21 1
 
0.2%
1.19 2
0.3%
1.18 1
 
0.2%
1.17 4
0.6%
1.16 1
 
0.2%
1.15 4
0.6%
1.14 1
 
0.2%
1.13 1
 
0.2%
1.12 1
 
0.2%

pp
Real number (ℝ)

Distinct538
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.89584
Minimum19
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.277878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile30.914167
Q133.209167
median35
Q336.565417
95-th percentile38.815833
Maximum42
Range23
Interquartile range (IQR)3.35625

Descriptive statistics

Standard deviation2.5131754
Coefficient of variation (CV)0.072019341
Kurtosis2.2934911
Mean34.89584
Median Absolute Deviation (MAD)1.6720833
Skewness-0.49857215
Sum22368.234
Variance6.3160507
MonotonicityNot monotonic
2023-12-29T05:52:45.330363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 14
 
2.2%
35 14
 
2.2%
36 11
 
1.7%
37 10
 
1.6%
39 8
 
1.2%
31 8
 
1.2%
40 8
 
1.2%
30 7
 
1.1%
34 7
 
1.1%
33 7
 
1.1%
Other values (528) 547
85.3%
ValueCountFrequency (%)
19 1
 
0.2%
24 1
 
0.2%
27.5 1
 
0.2%
29.46083333 1
 
0.2%
29.53333333 1
 
0.2%
29.65 1
 
0.2%
29.78333333 1
 
0.2%
30 7
1.1%
30.145 1
 
0.2%
30.15333333 1
 
0.2%
ValueCountFrequency (%)
42 2
 
0.3%
41.15 1
 
0.2%
41 2
 
0.3%
40 8
1.2%
39.35625 1
 
0.2%
39.31208333 1
 
0.2%
39.29083333 1
 
0.2%
39.13375 1
 
0.2%
39.01875 1
 
0.2%
39.00791667 1
 
0.2%

imc
Real number (ℝ)

Distinct591
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.397223
Minimum17.71542
Maximum44.581025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.382845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17.71542
5-th percentile20.730605
Q124.202271
median27.02581
Q330.180768
95-th percentile35.258951
Maximum44.581025
Range26.865605
Interquartile range (IQR)5.9784967

Descriptive statistics

Standard deviation4.5320756
Coefficient of variation (CV)0.16542099
Kurtosis0.7178845
Mean27.397223
Median Absolute Deviation (MAD)3.0156835
Skewness0.63435111
Sum17561.62
Variance20.539709
MonotonicityNot monotonic
2023-12-29T05:52:45.438060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.515625 3
 
0.5%
32.04994797 3
 
0.5%
26.2335558 3
 
0.5%
27.339912 3
 
0.5%
24.52434635 2
 
0.3%
25.45429082 2
 
0.3%
24.34175829 2
 
0.3%
25.86451247 2
 
0.3%
27.29322416 2
 
0.3%
21.79820239 2
 
0.3%
Other values (581) 617
96.3%
ValueCountFrequency (%)
17.7154195 1
0.2%
17.78324955 1
0.2%
18.32027714 1
0.2%
18.42024473 2
0.3%
18.48977356 1
0.2%
18.49851472 1
0.2%
18.80920625 1
0.2%
18.97357582 1
0.2%
19.03055706 1
0.2%
19.09481126 1
0.2%
ValueCountFrequency (%)
44.58102493 2
0.3%
44.11139456 1
0.2%
42.71982912 1
0.2%
41.74397032 1
0.2%
41.64469147 1
0.2%
41.22626201 1
0.2%
40.58441558 1
0.2%
40.51246537 1
0.2%
38.84765625 1
0.2%
38.47613426 1
0.2%

cct
Real number (ℝ)

Distinct557
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56014725
Minimum0.33766234
Maximum0.7987013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.489523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.33766234
5-th percentile0.44303797
Q10.51204819
median0.55688623
Q30.61046512
95-th percentile0.68831169
Maximum0.7987013
Range0.46103896
Interquartile range (IQR)0.098416924

Descriptive statistics

Standard deviation0.073496425
Coefficient of variation (CV)0.13120912
Kurtosis0.016394538
Mean0.56014725
Median Absolute Deviation (MAD)0.048209314
Skewness0.15129206
Sum359.05438
Variance0.0054017244
MonotonicityNot monotonic
2023-12-29T05:52:45.543829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 5
 
0.8%
0.5225806452 3
 
0.5%
0.5325443787 3
 
0.5%
0.5844155844 3
 
0.5%
0.5454545455 3
 
0.5%
0.5581395349 3
 
0.5%
0.6144578313 3
 
0.5%
0.5855263158 3
 
0.5%
0.7266666667 3
 
0.5%
0.5290322581 3
 
0.5%
Other values (547) 609
95.0%
ValueCountFrequency (%)
0.3376623377 1
0.2%
0.3419354839 1
0.2%
0.3618421053 1
0.2%
0.3703703704 1
0.2%
0.3725490196 1
0.2%
0.3966480447 1
0.2%
0.4131736527 1
0.2%
0.4157303371 1
0.2%
0.4171779141 1
0.2%
0.417721519 2
0.3%
ValueCountFrequency (%)
0.7987012987 1
 
0.2%
0.7756410256 1
 
0.2%
0.7655172414 1
 
0.2%
0.7565789474 2
0.3%
0.7388535032 1
 
0.2%
0.7358490566 1
 
0.2%
0.7266666667 3
0.5%
0.7236842105 1
 
0.2%
0.7232704403 1
 
0.2%
0.7225806452 1
 
0.2%

cnhdl
Real number (ℝ)

Distinct152
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.81903
Minimum46
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.596653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile93
Q1117
median137
Q3166
95-th percentile201
Maximum314
Range268
Interquartile range (IQR)49

Descriptive statistics

Standard deviation37.147657
Coefficient of variation (CV)0.26010299
Kurtosis1.4328983
Mean142.81903
Median Absolute Deviation (MAD)24
Skewness0.7125751
Sum91547
Variance1379.9484
MonotonicityNot monotonic
2023-12-29T05:52:45.651049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115 15
 
2.3%
118 13
 
2.0%
128 13
 
2.0%
127 12
 
1.9%
125 11
 
1.7%
102 11
 
1.7%
161 10
 
1.6%
130 10
 
1.6%
123 10
 
1.6%
97 10
 
1.6%
Other values (142) 526
82.1%
ValueCountFrequency (%)
46 1
0.2%
50 1
0.2%
58 1
0.2%
60 1
0.2%
61 1
0.2%
62 1
0.2%
63 2
0.3%
64 1
0.2%
69 1
0.2%
73 1
0.2%
ValueCountFrequency (%)
314 1
0.2%
306 1
0.2%
301 1
0.2%
270 1
0.2%
268 1
0.2%
253 1
0.2%
244 1
0.2%
241 1
0.2%
240 2
0.3%
233 1
0.2%

chdl
Real number (ℝ)

Distinct500
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4349402
Minimum1.4946237
Maximum10.515152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:45.704971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.4946237
5-th percentile2.8305085
Q13.5576923
median4.2820513
Q35.2222222
95-th percentile6.547619
Maximum10.515152
Range9.0205279
Interquartile range (IQR)1.6645299

Descriptive statistics

Standard deviation1.1857635
Coefficient of variation (CV)0.26736854
Kurtosis1.5153124
Mean4.4349402
Median Absolute Deviation (MAD)0.7918552
Skewness0.83618122
Sum2842.7967
Variance1.406035
MonotonicityNot monotonic
2023-12-29T05:52:45.755416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.615384615 5
 
0.8%
2.75 4
 
0.6%
3.462962963 4
 
0.6%
5.032258065 4
 
0.6%
4.272727273 4
 
0.6%
4 4
 
0.6%
5.266666667 4
 
0.6%
4.166666667 3
 
0.5%
3 3
 
0.5%
3.777777778 3
 
0.5%
Other values (490) 603
94.1%
ValueCountFrequency (%)
1.494623656 1
0.2%
1.967741935 1
0.2%
2.142857143 1
0.2%
2.230769231 1
0.2%
2.319148936 1
0.2%
2.322916667 1
0.2%
2.347222222 1
0.2%
2.383333333 2
0.3%
2.428571429 1
0.2%
2.487179487 1
0.2%
ValueCountFrequency (%)
10.51515152 1
0.2%
9.709677419 1
0.2%
8.75 1
0.2%
8.525 1
0.2%
8.357142857 1
0.2%
7.730769231 1
0.2%
7.666666667 1
0.2%
7.5 2
0.3%
7.481481481 1
0.2%
7.333333333 1
0.2%

Interactions

2023-12-29T05:52:43.086409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:33.944092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.732679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.417833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.310687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.145031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.889002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.529645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.338865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.989736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.640755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.231968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.862118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.478796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.128932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:33.993878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.776586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.504124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.358776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.204397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.939775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.575210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.384054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.037485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.682734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.276549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.910286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.521277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.393507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.037003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.816985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.552754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.407032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.312875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.982775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.618861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.429087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.081676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.723254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.318140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.953446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.562734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.436678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.083113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.868201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.610366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.476548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.368885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.028587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.848618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.474088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.129207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.765500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.362460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.001428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.605846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.479403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.127733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.916318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.663858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.536466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.416429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.071812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.893944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.520697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.174960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.808230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.410159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.046072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.649443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.523964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.174394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.959937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.725127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.586634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.462587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.118205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.940095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.567360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.224582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.851120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.455569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.091185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.694404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.567821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.220553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.003784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.841766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.636488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.511332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.165334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.984200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.615596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.273652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.895034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.502213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.136455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.739270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.609317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.265041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.047604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.896708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.704520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.558892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.210804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.026780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.661378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.320206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.937073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.544227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.177708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.781548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.654912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.468115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.094064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.946195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.770555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.611234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.260649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.074527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.711260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.369224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.982725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.591593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.223492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.829061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.701136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.517107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.143412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.004046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.822573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.664577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.307483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.122798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.761978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.417380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.027802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.639544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.270761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.876100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.742303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.559097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.189257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.056666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.882675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.708732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.352859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.164717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.807144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.460212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.067309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.681853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.312154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.916272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.786841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.602464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.235675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.110201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.951979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.754542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.398162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.207749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.851931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.506548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.109958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.725846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.354855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.959380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.827501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.643879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.304842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.166263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.019531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.797002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.442096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.251974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.898473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.550279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.150166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.774456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.394941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.001955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.870328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:34.689689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:35.359192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:36.259380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.088572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:37.847014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:38.485936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.296825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:39.945315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:40.596529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.192216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:41.819736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:42.437662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:43.045707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-12-29T05:52:45.803568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
epttstdgschdlccppimccctcnhdlchdlsu
e1.0000.060-0.2050.2830.1460.3280.3070.1160.206-0.1060.2060.2840.2920.1620.0990.000
p0.0601.0000.4530.3820.3390.2580.052-0.2970.8180.7960.8120.6160.1520.3250.3040.102
t-0.2050.4531.0000.0820.080-0.017-0.079-0.1510.1560.257-0.107-0.223-0.0340.0740.6570.071
ts0.2830.3820.0821.0000.7670.2730.120-0.1010.3640.2770.3760.3200.1560.2010.1190.071
td0.1460.3390.0800.7671.0000.1540.129-0.1160.3210.2640.3370.2870.1720.2120.0000.000
gs0.3280.258-0.0170.2730.1541.0000.064-0.1740.2590.1360.3130.2670.1340.2250.0860.000
c0.3070.052-0.0790.1200.1290.0641.0000.2950.1210.0980.1280.1520.9500.4930.1180.062
hdl0.116-0.297-0.151-0.101-0.116-0.1740.2951.000-0.231-0.336-0.243-0.1730.015-0.6420.2170.000
cc0.2060.8180.1560.3640.3210.2590.121-0.2311.0000.6740.8220.9150.2070.3260.1280.104
pp-0.1060.7960.2570.2770.2640.1360.098-0.3360.6741.0000.7410.5600.2060.3840.1480.078
imc0.2060.812-0.1070.3760.3370.3130.128-0.2430.8220.7411.0000.8500.2140.3360.0000.097
cct0.2840.616-0.2230.3200.2870.2670.152-0.1730.9150.5600.8501.0000.2200.2930.0790.116
cnhdl0.2920.152-0.0340.1560.1720.1340.9500.0150.2070.2060.2140.2201.0000.7200.0000.041
chdl0.1620.3250.0740.2010.2120.2250.493-0.6420.3260.3840.3360.2930.7201.0000.1940.076
s0.0990.3040.6570.1190.0000.0860.1180.2170.1280.1480.0000.0790.0000.1941.0000.098
u0.0000.1020.0710.0710.0000.0000.0620.0000.1040.0780.0970.1160.0410.0760.0981.000

Missing values

2023-12-29T05:52:43.937851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-29T05:52:44.036178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

esupttstdgschdlccppimccctcnhdlchdl
0181162.01.55101.070.085188.0530.81034.06458325.8064520.522581135.03.547170
1182170.01.6793.063.086155.0540.90034.88500025.0995020.538922101.02.870370
2191156.01.58109.073.093143.0600.67033.60000022.4323030.42405183.02.383333
3191152.21.59108.075.093143.0600.67033.60000020.6479170.42138483.02.383333
4191153.01.54118.072.083146.0520.73032.33583322.3477820.47402694.02.807692
5202162.01.6990.060.064163.0370.72032.27333321.7079230.426036126.04.405405
6202161.81.78106.072.099146.0350.74031.00000019.5051130.415730111.04.171429
7202160.51.78109.062.099146.0350.75031.00000019.0948110.421348111.04.171429
8202159.01.71117.074.084194.0460.78231.50250020.1771490.457310148.04.217391
9211148.11.5799.064.092167.0390.72530.39500019.5139760.461783128.04.282051
esupttstdgschdlccppimccctcnhdlchdl
631641474.0001.45120.070.0157302.0491.11000035.25750035.1961950.765517253.06.163265
632641460.0001.49100.070.090232.0640.82607332.49000027.0258100.554412168.03.625000
633691477.0001.52110.050.0138137.0251.10000030.00000033.3275620.723684112.05.480000
634702462.7001.53120.064.0208170.0530.57000033.00000026.7845700.372549117.03.207547
635701494.0001.51120.060.0176163.0320.76000036.00000041.2262620.503311131.05.093750
636352178.0001.69129.086.079221.0360.98200038.88416727.3099680.581065185.06.138889
637361499.0001.68125.088.0100189.0501.17000037.40000035.0765310.696429139.03.780000
638412171.8751.64100.064.081204.0330.88750039.00000026.7233050.541159171.06.181818
639422163.3001.69121.075.080230.0480.83000033.98750022.1630900.491124182.04.791667
640441493.6001.52120.071.0104175.0461.08000035.76500040.5124650.710526129.03.804348

Duplicate rows

Most frequently occurring

esupttstdgschdlccppimccctcnhdlchdl# duplicates
0222162.31.71106.076.076149.0340.8132.47458321.3057010.473684115.04.3823532
1581378.41.50160.080.095211.0431.0935.98333334.8444440.726667168.04.9069772